
#Importing all the libraries relevant to this investigation
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
import requests
import shutil
import json
import tweepy
import io
import os
import glob
import PIL.Image
from io import BytesIO
from PIL import Image


#gathering the data
twitter_archive=pd.read_csv('twitter_archive_enhanced.csv')


#Reading the dataframe
twitter_archive


#Downloading the file from the internet using the link provided
file_url = "https://d17h27t6h515a5.cloudfront.net/topher/2017/August/599fd2ad_image-predictions/image-predictions.tsv"
response = requests.get(file_url)

with open('image_predictions.tsv', mode ='wb') as file:
    file.write(response.content)

#Read TSV file
image_prediction = pd.read_csv('image_predictions.tsv', sep='\t' )


image_prediction


#Creating folder using python
folder_name = 'TwitterJson'
if not os.path.exists(folder_name):
    os.makedirs(folder_name)
  
#Getting the URL
url='https://video.udacity-data.com/topher/2018/November/5be5fb7d_tweet-json/tweet-json.txt'
response=requests.get(url)

#To view the raw data
response.content

#Saving the file to the computer
with open(os.path.join(folder_name, url.split('/')[-1]),mode='wb') as file:
        file.write(response.content)


#Define parameters
src= '/Users/brightonzongondi/Desktop/ALX DATA ANALYTICS/Data Wrangling/Twitter/TwitterJson/tweet-json.txt'
real_dst='/Users/brightonzongondi/Desktop/ALX DATA ANALYTICS/Data Wrangling/Twitter/tweet-json.txt'

#Use the Shutil function to move files
shutil.move(src,real_dst)

#Deleting the source folder named Twitter Json
myfile='/Users/brightonzongondi/Desktop/ALX DATA ANALYTICS/Data Wrangling/Twitter/TwitterJson'

## If file exists, delete it ##
if os.path.isfile(myfile):
    os.remove(myfile)


#Delete empty folder TwitterJson
empty_dir_path= '/Users/brightonzongondi/Desktop/ALX DATA ANALYTICS/Data Wrangling/Twitter/TwitterJson'
os.rmdir(empty_dir_path)

# Get the list of all files and directories to confirm if the empty folder has been deleted
path = '/Users/brightonzongondi/Desktop/ALX DATA ANALYTICS/Data Wrangling/Twitter'
dir_list = os.listdir(path)
 
print("Files and directories in '", path, "' :")
 
# prints all files
print(dir_list)

Files and directories in ' /Users/brightonzongondi/Desktop/ALX DATA ANALYTICS/Data Wrangling/Twitter ' :
['wrangle_report.html', 'wrangle_act.ipynb', '.DS_Store', 'Act_report.pdf', 'image_predictions.tsv', 'tweet-json.txt', 'twitter_archive_master.csv', 'twitter_archive_enhanced.csv', '.ipynb_checkpoints', 'tweet_json.txt']


#Renaming the saved file
old_name = 'tweet-json.txt'
new_name = 'tweet_json.txt'
os.rename(old_name, new_name)


#Extracting the required column
#Empty box
twitter_file=[]

#Reading the txt file using the Open function
with open('tweet_json.txt','r') as tweet_file:
    for line in tweet_file.readlines():
        single_tweet=json.loads(line) #Reading each json string into a dictionary
        #Accessing the required parameters
        tweet_id=single_tweet['id']
        retweet_cnt=single_tweet['retweet_count']
        fav_cnt=single_tweet['favorite_count']
        
        twitter_file.append({'id': tweet_id, 
                             'retweet_count':retweet_cnt, 
                             'favorite_count': fav_cnt })
        
tweet_data=pd.DataFrame(twitter_file, columns=['id',
                                              'retweet_count',
                                              'favorite_count'])


#Reading the dataframe
tweet_data


#Reading the dataframe
twitter_archive


#Getting the dataframe information
twitter_archive.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2356 entries, 0 to 2355
Data columns (total 17 columns):
 #   Column                      Non-Null Count  Dtype  
---  ------                      --------------  -----  
 0   tweet_id                    2356 non-null   int64  
 1   in_reply_to_status_id       78 non-null     float64
 2   in_reply_to_user_id         78 non-null     float64
 3   timestamp                   2356 non-null   object 
 4   source                      2356 non-null   object 
 5   text                        2356 non-null   object 
 6   retweeted_status_id         181 non-null    float64
 7   retweeted_status_user_id    181 non-null    float64
 8   retweeted_status_timestamp  181 non-null    object 
 9   expanded_urls               2297 non-null   object 
 10  rating_numerator            2356 non-null   int64  
 11  rating_denominator          2356 non-null   int64  
 12  name                        2356 non-null   object 
 13  doggo                       2356 non-null   object 
 14  floofer                     2356 non-null   object 
 15  pupper                      2356 non-null   object 
 16  puppo                       2356 non-null   object 
dtypes: float64(4), int64(3), object(10)
memory usage: 313.0+ KB


sum(twitter_archive['tweet_id'].duplicated())

0


#Statistics overview of the data
twitter_archive.describe()


#Shape of the data frame
twitter_archive.shape

(2356, 17)


#Checking for duplicate data in the Column tweet_id 
twitter_archive.tweet_id.duplicated().sum()

0


#Checking for the rating denominator. Since the rating score was set at 10, this denominator should be 10
twitter_archive[twitter_archive['rating_denominator'] >10 ]


#Abnormal rates in the rating_numerator Column.
twitter_archive[twitter_archive['rating_numerator'] >25]


#Reading the dataframe
image_prediction


#Getting the dataframe information
image_prediction.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2075 entries, 0 to 2074
Data columns (total 12 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   tweet_id  2075 non-null   int64  
 1   jpg_url   2075 non-null   object 
 2   img_num   2075 non-null   int64  
 3   p1        2075 non-null   object 
 4   p1_conf   2075 non-null   float64
 5   p1_dog    2075 non-null   bool   
 6   p2        2075 non-null   object 
 7   p2_conf   2075 non-null   float64
 8   p2_dog    2075 non-null   bool   
 9   p3        2075 non-null   object 
 10  p3_conf   2075 non-null   float64
 11  p3_dog    2075 non-null   bool   
dtypes: bool(3), float64(3), int64(2), object(4)
memory usage: 152.1+ KB


# Statistics overview of the data
image_prediction.describe()


#Shape of the data frame
image_prediction.shape

(2075, 12)


#Checking for duplicate data in the Column tweet_id 
image_prediction.tweet_id.duplicated().sum()

0


#Checking for duplicated images in the jpg_url Column
image_prediction.jpg_url.duplicated().sum()

66


#Check if there are lost images in img_num Column
image_prediction.img_num.isnull().sum()

0


#Reading the dataframe
tweet_data


#Getting the dataframe information
tweet_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2354 entries, 0 to 2353
Data columns (total 3 columns):
 #   Column          Non-Null Count  Dtype
---  ------          --------------  -----
 0   id              2354 non-null   int64
 1   retweet_count   2354 non-null   int64
 2   favorite_count  2354 non-null   int64
dtypes: int64(3)
memory usage: 55.3 KB


# Statistics overview of the data
tweet_data.describe()


#Shape of the data frame
tweet_data.shape

(2354, 3)


#Checking for duplicate data in the Column id 
tweet_data.id.duplicated().sum()

0


#Creating a copy of the twitter_archive Data source
twitter_clean=twitter_archive.copy()

#Creating a copy of the image_prediction Data source
img_clean=image_prediction .copy()

#Creating a copy of the tweet_data Data source
tweet_clean=tweet_data.copy()


#Checking the entries of Dog type 'doggo'
twitter_clean.doggo.value_counts()

None     2259
doggo      97
Name: doggo, dtype: int64


#Checking the entries of Dog type 'pupper'
twitter_clean.pupper.value_counts()

None      2099
pupper     257
Name: pupper, dtype: int64


#Checking the entries of Dog type 'floofer'
twitter_clean.floofer.value_counts()

None       2346
floofer      10
Name: floofer, dtype: int64


#Checking the entries of Dog type 'pupper'
twitter_clean.pupper.value_counts()

None      2099
pupper     257
Name: pupper, dtype: int64


#Defining a list for Dog types that will be retained whilst converting 'None' entries to Empty cells
Dog_typ = ['doggo','pupper', 'floofer', 'puppo' ]
for i in Dog_typ:
       twitter_clean[i] = twitter_clean[i].replace('None', '')
        
#Combine the Columns "Doggo,Floofer,Pupper, and Puppo" into one new Column Dog_type 
twitter_clean['dog_type'] = twitter_clean.doggo.str.cat(twitter_clean.floofer).str.cat(twitter_clean.pupper).str.cat(twitter_clean.puppo)

#Drop these Column "Doggo,Floofer,Pupper, and Puppo"
twitter_clean = twitter_clean.drop(['doggo','floofer','pupper','puppo'], axis = 1)

#Fill in the empty cells using the np.nan 
twitter_clean['dog_type'] = twitter_clean['dog_type'].replace('', np.nan)

#Verify if the Dog_type Column has been created
twitter_clean.sample(5)


#Checking the entries in Dog type 
twitter_clean.dog_type.value_counts()

pupper          245
doggo            83
puppo            29
doggopupper      12
floofer           9
doggopuppo        1
doggofloofer      1
Name: dog_type, dtype: int64


#Changing entries that have multiple dog types eg 'doggopupper' which is a combined entry of doggo and pupper
twitter_clean["dog_type"].replace({"doggopupper": "Multiple", "doggopuppo": "Multiple", "doggofloofer":"Multiple" }, inplace=True)

#Checking the entries in Dog type 
twitter_clean.dog_type.value_counts()

pupper      245
doggo        83
puppo        29
Multiple     14
floofer       9
Name: dog_type, dtype: int64


#Change the timestamp to a datetime format
twitter_clean['timestamp'] = pd.to_datetime(twitter_clean['timestamp'])


#Verify if the timestamp Column has been correctly dropped
twitter_clean.sample(5)


#Filter to exclude retweets inorder to only keep the original tweet
twitter_clean=twitter_clean[twitter_clean['retweeted_status_user_id'].isnull()]

#Verify if the retweeted_status_user_id contains any retweets
twitter_clean.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 2175 entries, 0 to 2355
Data columns (total 14 columns):
 #   Column                      Non-Null Count  Dtype              
---  ------                      --------------  -----              
 0   tweet_id                    2175 non-null   int64              
 1   in_reply_to_status_id       78 non-null     float64            
 2   in_reply_to_user_id         78 non-null     float64            
 3   timestamp                   2175 non-null   datetime64[ns, UTC]
 4   source                      2175 non-null   object             
 5   text                        2175 non-null   object             
 6   retweeted_status_id         0 non-null      float64            
 7   retweeted_status_user_id    0 non-null      float64            
 8   retweeted_status_timestamp  0 non-null      object             
 9   expanded_urls               2117 non-null   object             
 10  rating_numerator            2175 non-null   int64              
 11  rating_denominator          2175 non-null   int64              
 12  name                        2175 non-null   object             
 13  dog_type                    344 non-null    object             
dtypes: datetime64[ns, UTC](1), float64(4), int64(3), object(6)
memory usage: 254.9+ KB


#First list all columns in the twitter_clean Data frame
list(twitter_clean)

['tweet_id',
 'in_reply_to_status_id',
 'in_reply_to_user_id',
 'timestamp',
 'source',
 'text',
 'retweeted_status_id',
 'retweeted_status_user_id',
 'retweeted_status_timestamp',
 'expanded_urls',
 'rating_numerator',
 'rating_denominator',
 'name',
 'dog_type']


#Now drop the following unwanted Columns 
twitter_clean=twitter_clean.drop(['in_reply_to_status_id',
                        'in_reply_to_user_id',
                        'source',
                        'retweeted_status_id',
                        'retweeted_status_user_id',
                        'retweeted_status_timestamp',
                        'expanded_urls'], axis = 1)

#Now confirm if the above respective columns have been deleted
list(twitter_clean)

['tweet_id',
 'timestamp',
 'text',
 'rating_numerator',
 'rating_denominator',
 'name',
 'dog_type']


#Rename Column name from text to tweet
twitter_clean.rename(columns={'text':'tweet'},inplace=True)

#List the columns to verify if  Column name has been changed 
list(twitter_clean)

['tweet_id',
 'timestamp',
 'tweet',
 'rating_numerator',
 'rating_denominator',
 'name',
 'dog_type']


#twitter_clean.name.

#Get the length of the string of column in a dataframe
twitter_clean['name_length'] = twitter_clean['name'].apply(len)

#Checking the entries in name_length 
twitter_clean.name_length.value_counts()

4     980
5     402
6     310
7     207
3      95
8      69
1      56
9      25
2      19
10      6
12      3
11      2
14      1
Name: name_length, dtype: int64


twitter_clean.name_length

0       7
1       5
2       6
3       5
4       8
       ..
2351    4
2352    1
2353    1
2354    1
2355    4
Name: name_length, Length: 2175, dtype: int64


#Filtering for name string with "an"
twitter_names=(twitter_clean.loc[(twitter_clean['name_length']<=2) & 
                                 (twitter_clean['name']== 'a'),
                                 ['name','name_length']])

#Display the output of dataframe above
twitter_names


#Filtering for name string with "an"
twitter_names1=(twitter_clean.loc[(twitter_clean['name_length']<=3) & 
                                  (twitter_clean['name']) & 
                                  (twitter_clean['name']== 'an'),
                                  ['name','name_length']])

#Display the output of dataframe above
twitter_names1


#Filtering for name string with "the"
twitter_names2=(twitter_clean.loc[(twitter_clean['name_length']<=3) & 
                                  (twitter_clean['name']) & 
                                  (twitter_clean['name']== 'the'),
                                  ['name','name_length']])

#Display the output of dataframe above
twitter_names2


#Drop name entries with "a" 
for x in twitter_clean['name']:
    if x == "a":
        twitter_clean.name=twitter_clean.name.replace('a',np.nan)
        
    elif x == "an":
        twitter_clean.name=twitter_clean.name.replace('an',np.nan)
        
    elif x == "the":
        twitter_clean.name=twitter_clean.name.replace('the',np.nan)
        
        
#Convert "None" entries to "NaN"
twitter_clean['name'] = twitter_clean['name'].replace('None', np.nan)

#Capitalize the first letter of the Name in Column "name"
twitter_clean['name'] = twitter_clean['name'].str.capitalize()

#Check the different types of names contained in this data frame and also confirm if the first letter of the name has been Capitalized
twitter_clean['name'].value_counts()

Lucy          11
Charlie       11
Oliver        10
Cooper        10
Tucker         9
              ..
Wishes         1
Rose           1
Theo           1
Fido           1
Christoper     1
Name: name, Length: 952, dtype: int64


twitter_clean.name

0        Phineas
1          Tilly
2         Archie
3          Darla
4       Franklin
          ...   
2351         NaN
2352         NaN
2353         NaN
2354         NaN
2355         NaN
Name: name, Length: 2175, dtype: object


twitter_clean


#Checking for name string with "an"
twit_names=(twitter_clean.loc[(twitter_clean['name_length']<=2) & 
                                 (twitter_clean['name']== 'a'),
                                 ['name','name_length']])

#Display the output of dataframe above
twit_names.shape

(0, 2)


#Display the tail of data
twitter_clean.tail()


#Show the new copied data source
img_clean


#Drop the duplicate values in the jpg_url column
img_clean=img_clean.drop_duplicates(subset=['jpg_url'], keep='first')

#Check if there are still duplicate values in the jpg_url column
img_clean.jpg_url.duplicated().sum()

0


#Renaming Columns p1, p2, and p3
img_clean=img_clean.rename(columns={'p1':'1st_prediction', 
                                    'p1_conf': '1st_confidence', 
                                    'p1_dog': '1st_dog',
                                    'p2': '2nd_prediction',
                                    'p2_conf': '2nd_confidence', 
                                    'p2_dog': '2nd_dog',
                                    'p3': '3rd_prediction', 
                                    'p3_conf': '3rd_confidence', 
                                    'p3_dog': '3rd_dog'})

#Check the new column name s
list(img_clean)

['tweet_id',
 'jpg_url',
 'img_num',
 '1st_prediction',
 '1st_confidence',
 '1st_dog',
 '2nd_prediction',
 '2nd_confidence',
 '2nd_dog',
 '3rd_prediction',
 '3rd_confidence',
 '3rd_dog']


#Capitalize the first letters of the Prediction Columns
img_clean['1st_prediction']=img_clean['1st_prediction'].str.capitalize()
img_clean['2nd_prediction']=img_clean['2nd_prediction'].str.capitalize()
img_clean['3rd_prediction']=img_clean['3rd_prediction'].str.capitalize()

#Verify if the entries in the Prediction Columns have been capitalized
img_clean.sample(5)


#List all Columns
list(img_clean)

['tweet_id',
 'jpg_url',
 'img_num',
 '1st_prediction',
 '1st_confidence',
 '1st_dog',
 '2nd_prediction',
 '2nd_confidence',
 '2nd_dog',
 '3rd_prediction',
 '3rd_confidence',
 '3rd_dog']


#Drop unwated Column
img_clean=img_clean.drop("img_num",1)

#Verify if img_num column has been dropped
list(img_clean)

/Users/brightonzongondi/opt/anaconda3/envs/DATA-ANALYST/lib/python3.7/site-packages/ipykernel_launcher.py:2: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only

['tweet_id',
 'jpg_url',
 '1st_prediction',
 '1st_confidence',
 '1st_dog',
 '2nd_prediction',
 '2nd_confidence',
 '2nd_dog',
 '3rd_prediction',
 '3rd_confidence',
 '3rd_dog']


#Show the new Data frame
tweet_clean


#Rename the Column "id"  to "tweet_id"
tweet_clean = tweet_clean.rename(columns={'id':'tweet_id'})

#Verify if "id "column has been remaned to "tweet_id"
list(tweet_clean)

['tweet_id', 'retweet_count', 'favorite_count']


#Combine 'twitter_clean' with 'tweet_clean' using an inner joint
twitter_comb=pd.merge(twitter_clean, tweet_clean, how='inner',
                      on=None, left_on=None, right_on=None,left_index=False, 
                      right_index=False, sort=True,suffixes=('_x', '_y'), 
                      copy=True, indicator=False,validate=None)

#Show to confirm successful data joint
twitter_comb.sample(5)


#Combine 'img_clean' with 'twitter_comb' using an inner joint
twitter=pd.merge(img_clean, twitter_comb, how='inner', 
                 on=None, left_on=None, right_on=None,
                 left_index=False, right_index=False, 
                 sort=True,suffixes=('_x', '_y'), copy=True, 
                 indicator=False, validate=None)
#Show to confirm successful data joint
twitter.sample(5)


#Checking the shape of our final data
twitter.shape

(1994, 20)


#Save the cleaned and combined final version of our 'twitter' data as a CSV file
twitter.to_csv('twitter_archive_master.csv',index=False, encoding = 'utf-8')

#Check if the 'twitter.csv' file has been created
path = '/Users/brightonzongondi/Desktop/ALX DATA ANALYTICS/Data Wrangling/Twitter'
dir_list = os.listdir(path)
 
print("Files and directories in '", path, "' :")
 
# prints all files
print(dir_list)

Files and directories in ' /Users/brightonzongondi/Desktop/ALX DATA ANALYTICS/Data Wrangling/Twitter ' :
['wrangle_report.html', 'wrangle_act.ipynb', '.DS_Store', 'Act_report.pdf', 'image_predictions.tsv', 'twitter_archive_master.csv', 'twitter_archive_enhanced.csv', '.ipynb_checkpoints', 'tweet_json.txt']


#Read the 'twitter.csv' data file
twitter=pd.read_csv('twitter_archive_master.csv')

#List the data columns
list(twitter)

['tweet_id',
 'jpg_url',
 '1st_prediction',
 '1st_confidence',
 '1st_dog',
 '2nd_prediction',
 '2nd_confidence',
 '2nd_dog',
 '3rd_prediction',
 '3rd_confidence',
 '3rd_dog',
 'timestamp',
 'tweet',
 'rating_numerator',
 'rating_denominator',
 'name',
 'dog_type',
 'name_length',
 'retweet_count',
 'favorite_count']


#Plot 
plt.figure(figsize = (15,8))
ax = sns.barplot(x = twitter['1st_prediction'].value_counts()[0:10].index,
                y =twitter['1st_prediction'].value_counts()[0:10],
                data = twitter);
ax.set_xticklabels(ax.get_xticklabels(),rotation = 45, fontsize = 15);
plt.xlabel("Dog Breeds",fontsize = 18);
plt.ylabel("Prediction Hits",fontsize = 18);
plt.title("Popular Dog Breeds vs Prediction Hits",fontsize = 18);


#Filter for breed type 'Golden_retriever'
Top_dog=twitter[twitter['1st_prediction']=="Golden_retriever"]
#Sort for the top 5 dogs with the highest confidence of 1st_presdictions
Top_dog.sort_values('1st_prediction', ascending=False).head(5)


#The number 1 Golden Retriever Breed 
url = twitter.jpg_url[10]
r = requests.get(url)
Image.open(BytesIO(r.content))


#The number 2 Golden Retriever Breed 
url = twitter.jpg_url[1547]
r = requests.get(url)
Image.open(BytesIO(r.content))


#The number 3 Golden Retriever Breed 
url = twitter.jpg_url[1525]
r = requests.get(url)
Image.open(BytesIO(r.content))


#The number 4 Golden Retriever Breed 
url = twitter.jpg_url[1530]
r = requests.get(url)
Image.open(BytesIO(r.content))


#The number 5 Golden Retriever Breed 
url = twitter.jpg_url[1534]
r = requests.get(url)
Image.open(BytesIO(r.content))


#Filter for breed type 'Labrador_retriever'
Second_rated_dog=twitter[twitter['1st_prediction']=="Labrador_retriever"]
#Sort for the top 5 dogs with the highest confidence of 1st_presdictions
Second_rated_dog.sort_values('1st_prediction', ascending=False).head(5)


#The number 1 Labrador Retriever Breed 
url = twitter.jpg_url[41]
r = requests.get(url)
Image.open(BytesIO(r.content))


#The number 2 Labrador Retriever Breed 
url = twitter.jpg_url[1358]
r = requests.get(url)
Image.open(BytesIO(r.content))


#The number 3 Labrador Retriever Breed 
url = twitter.jpg_url[1527]
r = requests.get(url)
Image.open(BytesIO(r.content))


#The number 4 Labrador Retriever Breed 
url = twitter.jpg_url[1522]
r = requests.get(url)
Image.open(BytesIO(r.content))


#The number 5 Labrador Retriever Breed 
url = twitter.jpg_url[1508]
r = requests.get(url)
Image.open(BytesIO(r.content))


plt.figure(figsize = (15,8))
ax = sns.barplot(x = twitter['dog_type'].value_counts()[0:10].index,
                y =twitter['dog_type'].value_counts()[0:10],
                data = twitter);
ax.set_xticklabels(ax.get_xticklabels(),rotation = 0, fontsize = 15);
plt.xlabel("Dog Type",fontsize = 20);
plt.ylabel("Count",fontsize = 20);
plt.title("Popular Dog stage",fontsize = 20);


#Filter for top 5 most rated dog_type by likes
Pupper=twitter.sort_values('favorite_count',ascending=False);
Pupper=Pupper[['tweet_id','favorite_count','jpg_url','dog_type','1st_prediction']]
Pupper.head(2)


#The most liked dog type
url = twitter.jpg_url[1684]
r = requests.get(url)
Image.open(BytesIO(r.content))


#The Second most liked dog type
url = twitter.jpg_url[1218]
r = requests.get(url)
Image.open(BytesIO(r.content))


#Extracting Year for the Datetime in Timestamp
twitter['timestamp'] = pd.to_datetime(twitter['timestamp'])

twitter['year'] = twitter['timestamp'].dt.year

#twitter_date.groupby('timestamp' ['tweet_id'].count().plot(kind='pie', figsize=(10, 10));
twitter.sample(5)


#Show Voter Distribution In The Period Which The Votes Were Taken In A Pie Chart 
twitter.groupby("year")['tweet_id'].count().plot(kind='pie', figsize=(10, 10));


#Plot a Scatter plot to show the spread of retweet_counts over time against favorite counts
twitter.plot(y='retweet_count',x='favorite_count', style='s',alpha=0.9,figsize=(10,10),ylim=[10,50000]);
plt.legend("Retweet Counts",fontsize = 20);
plt.xlabel("Favorite Counts",fontsize = 20);
plt.ylabel("Retweet Counts",fontsize = 20);
plt.title("Virality Distribution Of Retweets",fontsize = 20);

	tweet_id	jpg_url	img_num	p1	p1_conf	p1_dog	p2	p2_conf	p2_dog	p3	p3_conf	p3_dog
0	666020888022790149	https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg	1	Welsh_springer_spaniel	0.465074	True	collie	0.156665	True	Shetland_sheepdog	0.061428	True
1	666029285002620928	https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg	1	redbone	0.506826	True	miniature_pinscher	0.074192	True	Rhodesian_ridgeback	0.072010	True
2	666033412701032449	https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg	1	German_shepherd	0.596461	True	malinois	0.138584	True	bloodhound	0.116197	True
3	666044226329800704	https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg	1	Rhodesian_ridgeback	0.408143	True	redbone	0.360687	True	miniature_pinscher	0.222752	True
4	666049248165822465	https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg	1	miniature_pinscher	0.560311	True	Rottweiler	0.243682	True	Doberman	0.154629	True
...	...	...	...	...	...	...	...	...	...	...	...	...
2070	891327558926688256	https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg	2	basset	0.555712	True	English_springer	0.225770	True	German_short-haired_pointer	0.175219	True
2071	891689557279858688	https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg	1	paper_towel	0.170278	False	Labrador_retriever	0.168086	True	spatula	0.040836	False
2072	891815181378084864	https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg	1	Chihuahua	0.716012	True	malamute	0.078253	True	kelpie	0.031379	True
2073	892177421306343426	https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg	1	Chihuahua	0.323581	True	Pekinese	0.090647	True	papillon	0.068957	True
2074	892420643555336193	https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg	1	orange	0.097049	False	bagel	0.085851	False	banana	0.076110	False

	id	retweet_count	favorite_count
0	892420643555336193	8853	39467
1	892177421306343426	6514	33819
2	891815181378084864	4328	25461
3	891689557279858688	8964	42908
4	891327558926688256	9774	41048
...	...	...	...
2349	666049248165822465	41	111
2350	666044226329800704	147	311
2351	666033412701032449	47	128
2352	666029285002620928	48	132
2353	666020888022790149	532	2535

	tweet_id	in_reply_to_status_id	in_reply_to_user_id	retweeted_status_id	retweeted_status_user_id	rating_numerator	rating_denominator
count	2.356000e+03	7.800000e+01	7.800000e+01	1.810000e+02	1.810000e+02	2356.000000	2356.000000
mean	7.427716e+17	7.455079e+17	2.014171e+16	7.720400e+17	1.241698e+16	13.126486	10.455433
std	6.856705e+16	7.582492e+16	1.252797e+17	6.236928e+16	9.599254e+16	45.876648	6.745237
min	6.660209e+17	6.658147e+17	1.185634e+07	6.661041e+17	7.832140e+05	0.000000	0.000000
25%	6.783989e+17	6.757419e+17	3.086374e+08	7.186315e+17	4.196984e+09	10.000000	10.000000
50%	7.196279e+17	7.038708e+17	4.196984e+09	7.804657e+17	4.196984e+09	11.000000	10.000000
75%	7.993373e+17	8.257804e+17	4.196984e+09	8.203146e+17	4.196984e+09	12.000000	10.000000
max	8.924206e+17	8.862664e+17	8.405479e+17	8.874740e+17	7.874618e+17	1776.000000	170.000000

	tweet_id	jpg_url	img_num	p1	p1_conf	p1_dog	p2	p2_conf	p2_dog	p3	p3_conf	p3_dog
0	666020888022790149	https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg	1	Welsh_springer_spaniel	0.465074	True	collie	0.156665	True	Shetland_sheepdog	0.061428	True
1	666029285002620928	https://pbs.twimg.com/media/CT42GRgUYAA5iDo.jpg	1	redbone	0.506826	True	miniature_pinscher	0.074192	True	Rhodesian_ridgeback	0.072010	True
2	666033412701032449	https://pbs.twimg.com/media/CT4521TWwAEvMyu.jpg	1	German_shepherd	0.596461	True	malinois	0.138584	True	bloodhound	0.116197	True
3	666044226329800704	https://pbs.twimg.com/media/CT5Dr8HUEAA-lEu.jpg	1	Rhodesian_ridgeback	0.408143	True	redbone	0.360687	True	miniature_pinscher	0.222752	True
4	666049248165822465	https://pbs.twimg.com/media/CT5IQmsXIAAKY4A.jpg	1	miniature_pinscher	0.560311	True	Rottweiler	0.243682	True	Doberman	0.154629	True
...	...	...	...	...	...	...	...	...	...	...	...	...
2070	891327558926688256	https://pbs.twimg.com/media/DF6hr6BUMAAzZgT.jpg	2	basset	0.555712	True	English_springer	0.225770	True	German_short-haired_pointer	0.175219	True
2071	891689557279858688	https://pbs.twimg.com/media/DF_q7IAWsAEuuN8.jpg	1	paper_towel	0.170278	False	Labrador_retriever	0.168086	True	spatula	0.040836	False
2072	891815181378084864	https://pbs.twimg.com/media/DGBdLU1WsAANxJ9.jpg	1	Chihuahua	0.716012	True	malamute	0.078253	True	kelpie	0.031379	True
2073	892177421306343426	https://pbs.twimg.com/media/DGGmoV4XsAAUL6n.jpg	1	Chihuahua	0.323581	True	Pekinese	0.090647	True	papillon	0.068957	True
2074	892420643555336193	https://pbs.twimg.com/media/DGKD1-bXoAAIAUK.jpg	1	orange	0.097049	False	bagel	0.085851	False	banana	0.076110	False

	tweet_id	img_num	p1_conf	p2_conf	p3_conf
count	2.075000e+03	2075.000000	2075.000000	2.075000e+03	2.075000e+03
mean	7.384514e+17	1.203855	0.594548	1.345886e-01	6.032417e-02
std	6.785203e+16	0.561875	0.271174	1.006657e-01	5.090593e-02
min	6.660209e+17	1.000000	0.044333	1.011300e-08	1.740170e-10
25%	6.764835e+17	1.000000	0.364412	5.388625e-02	1.622240e-02
50%	7.119988e+17	1.000000	0.588230	1.181810e-01	4.944380e-02
75%	7.932034e+17	1.000000	0.843855	1.955655e-01	9.180755e-02
max	8.924206e+17	4.000000	1.000000	4.880140e-01	2.734190e-01

	tweet_id	in_reply_to_status_id	in_reply_to_user_id	timestamp	source	text	retweeted_status_id	retweeted_status_user_id	retweeted_status_timestamp	expanded_urls	rating_numerator	rating_denominator	name	doggo	floofer	pupper	puppo
0	892420643555336193	NaN	NaN	2017-08-01 16:23:56 +0000	<a href="http://twitter.com/download/iphone" r...	This is Phineas. He's a mystical boy. Only eve...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/892420643...	13	10	Phineas	None	None	None	None
1	892177421306343426	NaN	NaN	2017-08-01 00:17:27 +0000	<a href="http://twitter.com/download/iphone" r...	This is Tilly. She's just checking pup on you....	NaN	NaN	NaN	https://twitter.com/dog_rates/status/892177421...	13	10	Tilly	None	None	None	None
2	891815181378084864	NaN	NaN	2017-07-31 00:18:03 +0000	<a href="http://twitter.com/download/iphone" r...	This is Archie. He is a rare Norwegian Pouncin...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/891815181...	12	10	Archie	None	None	None	None
3	891689557279858688	NaN	NaN	2017-07-30 15:58:51 +0000	<a href="http://twitter.com/download/iphone" r...	This is Darla. She commenced a snooze mid meal...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/891689557...	13	10	Darla	None	None	None	None
4	891327558926688256	NaN	NaN	2017-07-29 16:00:24 +0000	<a href="http://twitter.com/download/iphone" r...	This is Franklin. He would like you to stop ca...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/891327558...	12	10	Franklin	None	None	None	None
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2351	666049248165822465	NaN	NaN	2015-11-16 00:24:50 +0000	<a href="http://twitter.com/download/iphone" r...	Here we have a 1949 1st generation vulpix. Enj...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/666049248...	5	10	None	None	None	None	None
2352	666044226329800704	NaN	NaN	2015-11-16 00:04:52 +0000	<a href="http://twitter.com/download/iphone" r...	This is a purebred Piers Morgan. Loves to Netf...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/666044226...	6	10	a	None	None	None	None
2353	666033412701032449	NaN	NaN	2015-11-15 23:21:54 +0000	<a href="http://twitter.com/download/iphone" r...	Here is a very happy pup. Big fan of well-main...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/666033412...	9	10	a	None	None	None	None
2354	666029285002620928	NaN	NaN	2015-11-15 23:05:30 +0000	<a href="http://twitter.com/download/iphone" r...	This is a western brown Mitsubishi terrier. Up...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/666029285...	7	10	a	None	None	None	None
2355	666020888022790149	NaN	NaN	2015-11-15 22:32:08 +0000	<a href="http://twitter.com/download/iphone" r...	Here we have a Japanese Irish Setter. Lost eye...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/666020888...	8	10	None	None	None	None	None

	tweet_id	in_reply_to_status_id	in_reply_to_user_id	timestamp	source	text	retweeted_status_id	retweeted_status_user_id	retweeted_status_timestamp	expanded_urls	rating_numerator	rating_denominator	name	doggo	floofer	pupper	puppo
342	832088576586297345	8.320875e+17	3.058208e+07	2017-02-16 04:45:50 +0000	<a href="http://twitter.com/download/iphone" r...	@docmisterio account started on 11/15/15	NaN	NaN	NaN	NaN	11	15	None	None	None	None	None
433	820690176645140481	NaN	NaN	2017-01-15 17:52:40 +0000	<a href="http://twitter.com/download/iphone" r...	The floofs have been released I repeat the flo...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/820690176...	84	70	None	None	None	None	None
784	775096608509886464	NaN	NaN	2016-09-11 22:20:06 +0000	<a href="http://twitter.com/download/iphone" r...	RT @dog_rates: After so many requests, this is...	7.403732e+17	4.196984e+09	2016-06-08 02:41:38 +0000	https://twitter.com/dog_rates/status/740373189...	9	11	None	None	None	None	None
902	758467244762497024	NaN	NaN	2016-07-28 01:00:57 +0000	<a href="http://twitter.com/download/iphone" r...	Why does this never happen at my front door......	NaN	NaN	NaN	https://twitter.com/dog_rates/status/758467244...	165	150	None	None	None	None	None
1068	740373189193256964	NaN	NaN	2016-06-08 02:41:38 +0000	<a href="http://twitter.com/download/iphone" r...	After so many requests, this is Bretagne. She ...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/740373189...	9	11	None	None	None	None	None
1120	731156023742988288	NaN	NaN	2016-05-13 16:15:54 +0000	<a href="http://twitter.com/download/iphone" r...	Say hello to this unbelievably well behaved sq...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/731156023...	204	170	this	None	None	None	None
1165	722974582966214656	NaN	NaN	2016-04-21 02:25:47 +0000	<a href="http://twitter.com/download/iphone" r...	Happy 4/20 from the squad! 13/10 for all https...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/722974582...	4	20	None	None	None	None	None
1202	716439118184652801	NaN	NaN	2016-04-03 01:36:11 +0000	<a href="http://twitter.com/download/iphone" r...	This is Bluebert. He just saw that both #Final...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/716439118...	50	50	Bluebert	None	None	None	None
1228	713900603437621249	NaN	NaN	2016-03-27 01:29:02 +0000	<a href="http://twitter.com/download/iphone" r...	Happy Saturday here's 9 puppers on a bench. 99...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/713900603...	99	90	None	None	None	None	None
1254	710658690886586372	NaN	NaN	2016-03-18 02:46:49 +0000	<a href="http://twitter.com/download/iphone" r...	Here's a brigade of puppers. All look very pre...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/710658690...	80	80	None	None	None	None	None
1274	709198395643068416	NaN	NaN	2016-03-14 02:04:08 +0000	<a href="http://twitter.com/download/iphone" r...	From left to right:\nCletus, Jerome, Alejandro...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/709198395...	45	50	None	None	None	None	None
1351	704054845121142784	NaN	NaN	2016-02-28 21:25:30 +0000	<a href="http://twitter.com/download/iphone" r...	Here is a whole flock of puppers. 60/50 I'll ...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/704054845...	60	50	a	None	None	None	None
1433	697463031882764288	NaN	NaN	2016-02-10 16:51:59 +0000	<a href="http://twitter.com/download/iphone" r...	Happy Wednesday here's a bucket of pups. 44/40...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/697463031...	44	40	None	None	None	None	None
1598	686035780142297088	6.860340e+17	4.196984e+09	2016-01-10 04:04:10 +0000	<a href="http://twitter.com/download/iphone" r...	Yes I do realize a rating of 4/20 would've bee...	NaN	NaN	NaN	NaN	4	20	None	None	None	None	None
1634	684225744407494656	6.842229e+17	4.196984e+09	2016-01-05 04:11:44 +0000	<a href="http://twitter.com/download/iphone" r...	Two sneaky puppers were not initially seen, mo...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/684225744...	143	130	None	None	None	None	None
1635	684222868335505415	NaN	NaN	2016-01-05 04:00:18 +0000	<a href="http://twitter.com/download/iphone" r...	Someone help the girl is being mugged. Several...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/684222868...	121	110	None	None	None	None	None
1662	682962037429899265	NaN	NaN	2016-01-01 16:30:13 +0000	<a href="http://twitter.com/download/iphone" r...	This is Darrel. He just robbed a 7/11 and is i...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/682962037...	7	11	Darrel	None	None	None	None
1663	682808988178739200	6.827884e+17	4.196984e+09	2016-01-01 06:22:03 +0000	<a href="http://twitter.com/download/iphone" r...	I'm aware that I could've said 20/16, but here...	NaN	NaN	NaN	NaN	20	16	None	None	None	None	None
1779	677716515794329600	NaN	NaN	2015-12-18 05:06:23 +0000	<a href="http://twitter.com/download/iphone" r...	IT'S PUPPERGEDDON. Total of 144/120 ...I think...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/677716515...	144	120	None	None	None	None	None
1843	675853064436391936	NaN	NaN	2015-12-13 01:41:41 +0000	<a href="http://twitter.com/download/iphone" r...	Here we have an entire platoon of puppers. Tot...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/675853064...	88	80	None	None	None	None	None

	tweet_id	in_reply_to_status_id	in_reply_to_user_id	timestamp	source	text	retweeted_status_id	retweeted_status_user_id	retweeted_status_timestamp	expanded_urls	rating_numerator	rating_denominator	name	doggo	floofer	pupper	puppo
188	855862651834028034	8.558616e+17	1.943518e+08	2017-04-22 19:15:32 +0000	<a href="http://twitter.com/download/iphone" r...	@dhmontgomery We also gave snoop dogg a 420/10...	NaN	NaN	NaN	NaN	420	10	None	None	None	None	None
189	855860136149123072	8.558585e+17	1.361572e+07	2017-04-22 19:05:32 +0000	<a href="http://twitter.com/download/iphone" r...	@s8n You tried very hard to portray this good ...	NaN	NaN	NaN	NaN	666	10	None	None	None	None	None
290	838150277551247360	8.381455e+17	2.195506e+07	2017-03-04 22:12:52 +0000	<a href="http://twitter.com/download/iphone" r...	@markhoppus 182/10	NaN	NaN	NaN	NaN	182	10	None	None	None	None	None
313	835246439529840640	8.352460e+17	2.625958e+07	2017-02-24 21:54:03 +0000	<a href="http://twitter.com/download/iphone" r...	@jonnysun @Lin_Manuel ok jomny I know you're e...	NaN	NaN	NaN	NaN	960	0	None	None	None	None	None
340	832215909146226688	NaN	NaN	2017-02-16 13:11:49 +0000	<a href="http://twitter.com/download/iphone" r...	RT @dog_rates: This is Logan, the Chow who liv...	7.867091e+17	4.196984e+09	2016-10-13 23:23:56 +0000	https://twitter.com/dog_rates/status/786709082...	75	10	Logan	None	None	None	None
433	820690176645140481	NaN	NaN	2017-01-15 17:52:40 +0000	<a href="http://twitter.com/download/iphone" r...	The floofs have been released I repeat the flo...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/820690176...	84	70	None	None	None	None	None
695	786709082849828864	NaN	NaN	2016-10-13 23:23:56 +0000	<a href="http://twitter.com/download/iphone" r...	This is Logan, the Chow who lived. He solemnly...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/786709082...	75	10	Logan	None	None	None	None
763	778027034220126208	NaN	NaN	2016-09-20 00:24:34 +0000	<a href="http://twitter.com/download/iphone" r...	This is Sophie. She's a Jubilant Bush Pupper. ...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/778027034...	27	10	Sophie	None	None	pupper	None
902	758467244762497024	NaN	NaN	2016-07-28 01:00:57 +0000	<a href="http://twitter.com/download/iphone" r...	Why does this never happen at my front door......	NaN	NaN	NaN	https://twitter.com/dog_rates/status/758467244...	165	150	None	None	None	None	None
979	749981277374128128	NaN	NaN	2016-07-04 15:00:45 +0000	<a href="https://about.twitter.com/products/tw...	This is Atticus. He's quite simply America af....	NaN	NaN	NaN	https://twitter.com/dog_rates/status/749981277...	1776	10	Atticus	None	None	None	None
1120	731156023742988288	NaN	NaN	2016-05-13 16:15:54 +0000	<a href="http://twitter.com/download/iphone" r...	Say hello to this unbelievably well behaved sq...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/731156023...	204	170	this	None	None	None	None
1202	716439118184652801	NaN	NaN	2016-04-03 01:36:11 +0000	<a href="http://twitter.com/download/iphone" r...	This is Bluebert. He just saw that both #Final...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/716439118...	50	50	Bluebert	None	None	None	None
1228	713900603437621249	NaN	NaN	2016-03-27 01:29:02 +0000	<a href="http://twitter.com/download/iphone" r...	Happy Saturday here's 9 puppers on a bench. 99...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/713900603...	99	90	None	None	None	None	None
1254	710658690886586372	NaN	NaN	2016-03-18 02:46:49 +0000	<a href="http://twitter.com/download/iphone" r...	Here's a brigade of puppers. All look very pre...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/710658690...	80	80	None	None	None	None	None
1274	709198395643068416	NaN	NaN	2016-03-14 02:04:08 +0000	<a href="http://twitter.com/download/iphone" r...	From left to right:\nCletus, Jerome, Alejandro...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/709198395...	45	50	None	None	None	None	None
1351	704054845121142784	NaN	NaN	2016-02-28 21:25:30 +0000	<a href="http://twitter.com/download/iphone" r...	Here is a whole flock of puppers. 60/50 I'll ...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/704054845...	60	50	a	None	None	None	None
1433	697463031882764288	NaN	NaN	2016-02-10 16:51:59 +0000	<a href="http://twitter.com/download/iphone" r...	Happy Wednesday here's a bucket of pups. 44/40...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/697463031...	44	40	None	None	None	None	None
1634	684225744407494656	6.842229e+17	4.196984e+09	2016-01-05 04:11:44 +0000	<a href="http://twitter.com/download/iphone" r...	Two sneaky puppers were not initially seen, mo...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/684225744...	143	130	None	None	None	None	None
1635	684222868335505415	NaN	NaN	2016-01-05 04:00:18 +0000	<a href="http://twitter.com/download/iphone" r...	Someone help the girl is being mugged. Several...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/684222868...	121	110	None	None	None	None	None
1712	680494726643068929	NaN	NaN	2015-12-25 21:06:00 +0000	<a href="http://twitter.com/download/iphone" r...	Here we have uncovered an entire battalion of ...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/680494726...	26	10	None	None	None	None	None
1779	677716515794329600	NaN	NaN	2015-12-18 05:06:23 +0000	<a href="http://twitter.com/download/iphone" r...	IT'S PUPPERGEDDON. Total of 144/120 ...I think...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/677716515...	144	120	None	None	None	None	None
1843	675853064436391936	NaN	NaN	2015-12-13 01:41:41 +0000	<a href="http://twitter.com/download/iphone" r...	Here we have an entire platoon of puppers. Tot...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/675853064...	88	80	None	None	None	None	None
2074	670842764863651840	NaN	NaN	2015-11-29 05:52:33 +0000	<a href="http://twitter.com/download/iphone" r...	After so many requests... here you go.\n\nGood...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/670842764...	420	10	None	None	None	None	None

	id	retweet_count	favorite_count
count	2.354000e+03	2354.000000	2354.000000
mean	7.426978e+17	3164.797366	8080.968564
std	6.852812e+16	5284.770364	11814.771334
min	6.660209e+17	0.000000	0.000000
25%	6.783975e+17	624.500000	1415.000000
50%	7.194596e+17	1473.500000	3603.500000
75%	7.993058e+17	3652.000000	10122.250000
max	8.924206e+17	79515.000000	132810.000000

	tweet_id	in_reply_to_status_id	in_reply_to_user_id	timestamp	source	text	retweeted_status_id	retweeted_status_user_id	retweeted_status_timestamp	expanded_urls	rating_numerator	rating_denominator	name	dog_type
329	833479644947025920	NaN	NaN	2017-02-20 00:53:27 +0000	<a href="http://twitter.com/download/iphone" r...	This is Poppy. She just arrived. 13/10 would s...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/833479644...	13	10	Poppy	NaN
277	840370681858686976	NaN	NaN	2017-03-11 01:15:58 +0000	<a href="http://twitter.com/download/iphone" r...	You have been visited by the magical sugar jar...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/840370681...	13	10	None	NaN
1753	678800283649069056	NaN	NaN	2015-12-21 04:52:53 +0000	<a href="http://twitter.com/download/iphone" r...	Here's a pupper with some mean tan lines. Snaz...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/678800283...	12	10	None	pupper
1495	692535307825213440	NaN	NaN	2016-01-28 02:30:58 +0000	<a href="http://twitter.com/download/iphone" r...	This is Amber. She's a Fetty Woof. 10/10 would...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/692535307...	10	10	Amber	NaN
2203	668641109086707712	NaN	NaN	2015-11-23 04:03:57 +0000	<a href="http://twitter.com/download/iphone" r...	Meet Hanz. He heard some thunder. 10/10 https:...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/668641109...	10	10	Hanz	NaN

	tweet_id	in_reply_to_status_id	in_reply_to_user_id	timestamp	source	text	retweeted_status_id	retweeted_status_user_id	retweeted_status_timestamp	expanded_urls	rating_numerator	rating_denominator	name	dog_type
1877	675109292475830276	NaN	NaN	2015-12-11 00:26:12+00:00	<a href="http://twitter.com/download/iphone" r...	C'mon guys. We've been over this. We only rate...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/675109292...	9	10	a	NaN
641	793226087023144960	NaN	NaN	2016-10-31 23:00:11+00:00	<a href="http://twitter.com/download/iphone" r...	This is Juno. She spooked me up real good, but...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/793226087...	11	10	Juno	NaN
1354	703631701117943808	NaN	NaN	2016-02-27 17:24:05+00:00	<a href="http://twitter.com/download/iphone" r...	This is Bella. Based on this picture she's at ...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/703631701...	11	10	Bella	NaN
662	790987426131050500	NaN	NaN	2016-10-25 18:44:32+00:00	<a href="http://twitter.com/download/iphone" r...	This is Misty. She has a cowboy hat on her nos...	NaN	NaN	NaN	https://twitter.com/dog_rates/status/790987426...	12	10	Misty	NaN
1313	706904523814649856	NaN	NaN	2016-03-07 18:09:06+00:00	<a href="http://vine.co" rel="nofollow">Vine -...	Ever seen a dog pet another dog? Both 13/10 tr...	NaN	NaN	NaN	https://vine.co/v/iXQAm5Lrgrh	13	10	None	NaN

	name	name_length
56	a	1
649	a	1
801	a	1
1002	a	1
1004	a	1
1017	a	1
1049	a	1
1193	a	1
1207	a	1
1340	a	1
1351	a	1
1361	a	1
1368	a	1
1382	a	1
1499	a	1
1737	a	1
1785	a	1
1853	a	1
1854	a	1
1877	a	1
1878	a	1
1923	a	1
1941	a	1
1955	a	1
1994	a	1
2034	a	1
2066	a	1
2116	a	1
2125	a	1
2128	a	1
2146	a	1
2153	a	1
2161	a	1
2191	a	1
2198	a	1
2211	a	1
2218	a	1
2222	a	1
2235	a	1
2249	a	1
2255	a	1
2264	a	1
2273	a	1
2287	a	1
2304	a	1
2311	a	1
2314	a	1
2327	a	1
2334	a	1
2347	a	1
2348	a	1
2350	a	1
2352	a	1
2353	a	1
2354	a	1

	name	name_length
1527	the	3
1603	the	3
1797	the	3
1815	the	3
2037	the	3
2212	the	3
2345	the	3
2346	the	3

	tweet_id	timestamp	tweet	rating_numerator	rating_denominator	name	dog_type	name_length
0	892420643555336193	2017-08-01 16:23:56+00:00	This is Phineas. He's a mystical boy. Only eve...	13	10	Phineas	NaN	7
1	892177421306343426	2017-08-01 00:17:27+00:00	This is Tilly. She's just checking pup on you....	13	10	Tilly	NaN	5
2	891815181378084864	2017-07-31 00:18:03+00:00	This is Archie. He is a rare Norwegian Pouncin...	12	10	Archie	NaN	6
3	891689557279858688	2017-07-30 15:58:51+00:00	This is Darla. She commenced a snooze mid meal...	13	10	Darla	NaN	5
4	891327558926688256	2017-07-29 16:00:24+00:00	This is Franklin. He would like you to stop ca...	12	10	Franklin	NaN	8
...	...	...	...	...	...	...	...	...
2351	666049248165822465	2015-11-16 00:24:50+00:00	Here we have a 1949 1st generation vulpix. Enj...	5	10	NaN	NaN	4
2352	666044226329800704	2015-11-16 00:04:52+00:00	This is a purebred Piers Morgan. Loves to Netf...	6	10	NaN	NaN	1
2353	666033412701032449	2015-11-15 23:21:54+00:00	Here is a very happy pup. Big fan of well-main...	9	10	NaN	NaN	1
2354	666029285002620928	2015-11-15 23:05:30+00:00	This is a western brown Mitsubishi terrier. Up...	7	10	NaN	NaN	1
2355	666020888022790149	2015-11-15 22:32:08+00:00	Here we have a Japanese Irish Setter. Lost eye...	8	10	NaN	NaN	4

	tweet_id	jpg_url	img_num	1st_prediction	1st_confidence	1st_dog	2nd_prediction	2nd_confidence	2nd_dog	3rd_prediction	3rd_confidence	3rd_dog
536	676946864479084545	https://pbs.twimg.com/media/CWT_lOQWUAAXPaY.jpg	1	Pekinese	0.752707	True	Golden_retriever	0.055655	True	Great_pyrenees	0.041018	True
1166	735635087207878657	https://pbs.twimg.com/media/CjWANBlVAAAaN-a.jpg	1	Pug	0.891871	True	Goose	0.014377	False	Fur_coat	0.008451	False
1957	865006731092295680	https://pbs.twimg.com/media/DAEfCFXUIAA1uqj.jpg	1	Pembroke	0.989882	True	Cardigan	0.009906	True	Basenji	0.000135	True
1457	777684233540206592	https://pbs.twimg.com/media/CsrjryzWgAAZY00.jpg	1	Cocker_spaniel	0.253442	True	Golden_retriever	0.162850	True	Otterhound	0.110921	True
926	702598099714314240	https://pbs.twimg.com/media/CcAhPevW8AAoknv.jpg	1	Kelpie	0.219179	True	Badger	0.133584	False	Siamese_cat	0.074440	False

	name	name_length
1025	an	2
1362	an	2
2204	an	2
2333	an	2
2335	an	2
2349	an	2

	tweet_id	timestamp	tweet	rating_numerator	rating_denominator	name	dog_type	name_length	retweet_count	favorite_count
1109	711743778164514816	2016-03-21 02:38:34+00:00	Meet Roosevelt. He's calculating the best case...	11	10	Roosevelt	NaN	9	1101	3095
1982	849336543269576704	2017-04-04 19:03:06+00:00	At first I thought this was a dog because of t...	11	10	NaN	NaN	4	2101	12240
1967	846505985330044928	2017-03-27 23:35:28+00:00	THIS WAS NOT HIS FAULT HE HAD NO IDEA. 11/10 S...	11	10	NaN	NaN	4	3492	15304
606	679111216690831360	2015-12-22 01:28:25+00:00	This is officially the greatest yawn of all ti...	12	10	Officially	NaN	10	2893	6514
1949	842765311967449089	2017-03-17 15:51:22+00:00	Meet Indie. She's not a fan of baths but she's...	12	10	Indie	NaN	5	1439	7321

	tweet_id	jpg_url	1st_prediction	1st_confidence	1st_dog	2nd_prediction	2nd_confidence	2nd_dog	3rd_prediction	3rd_confidence	3rd_dog	timestamp	tweet	rating_numerator	rating_denominator	name	dog_type	name_length	retweet_count	favorite_count
1487	786363235746385920	https://pbs.twimg.com/media/Cum5LlfWAAAyPcS.jpg	Golden_retriever	0.929266	True	Labrador_retriever	0.062867	True	Saluki	0.002157	True	2016-10-13 00:29:39+00:00	This is Rizzo. He has many talents. A true ren...	13	10	Rizzo	doggo	5	4072	12189
264	670804601705242624	https://pbs.twimg.com/media/CU8tOJZWUAAlNoF.jpg	Pomeranian	0.868560	True	Pekinese	0.090129	True	Chow	0.021722	True	2015-11-29 03:20:54+00:00	Meet Mason. He's a total frat boy. Pretends to...	10	10	Mason	NaN	5	1035	2098
865	697575480820686848	https://pbs.twimg.com/media/Ca5JMvMUsAAGMll.jpg	Siamese_cat	0.256698	False	Whippet	0.119805	True	Bull_mastiff	0.102595	True	2016-02-11 00:18:49+00:00	This is Ole. He's not sure how to gravity. 8/1...	8	10	Ole	NaN	3	572	2250
1410	772193107915964416	https://pbs.twimg.com/media/Crdhh_1XEAAHKHi.jpg	Pembroke	0.367945	True	Chihuahua	0.223522	True	Pekinese	0.164871	True	2016-09-03 22:02:38+00:00	This is Willem. He's a Penn State pupper. Thin...	12	10	Willem	pupper	6	1612	6665
335	672231046314901505	https://pbs.twimg.com/media/CVQ-kfWWoAAXV15.jpg	Killer_whale	0.823919	False	Grey_whale	0.036601	False	Hammerhead	0.029522	False	2015-12-03 01:49:05+00:00	Exotic underwater dog here. Very shy. Wont ret...	5	10	NaN	NaN	4	1089	1956

	tweet_id	jpg_url	1st_prediction	1st_confidence	1st_dog	2nd_prediction	2nd_confidence	2nd_dog	3rd_prediction	3rd_confidence	3rd_dog	timestamp	tweet	rating_numerator	rating_denominator	name	dog_type	name_length	retweet_count	favorite_count
10	666063827256086533	https://pbs.twimg.com/media/CT5Vg_wXIAAXfnj.jpg	Golden_retriever	0.775930	True	Tibetan_mastiff	0.093718	True	Labrador_retriever	0.072427	True	2015-11-16 01:22:45+00:00	This is the happiest dog you will ever see. Ve...	10	10	NaN	NaN	3	232	496
1547	796149749086875649	https://pbs.twimg.com/media/Cwx99rpW8AMk_Ie.jpg	Golden_retriever	0.600276	True	Labrador_retriever	0.140798	True	Seat_belt	0.087355	False	2016-11-09 00:37:46+00:00	This is Ruby. She just turned on the news. Off...	11	10	Ruby	NaN	4	16628	36177
1525	793165685325201412	https://pbs.twimg.com/media/CwHj-jGWAAAnsny.jpg	Golden_retriever	0.946224	True	Labrador_retriever	0.036477	True	Doormat	0.002353	False	2016-10-31 19:00:10+00:00	This is Benji. He's Air Bud. It's a low effort...	12	10	Benji	NaN	5	3238	10478
1530	793241302385262592	https://pbs.twimg.com/media/CwIougTWcAAMLyq.jpg	Golden_retriever	0.559308	True	Labrador_retriever	0.390222	True	Cocker_spaniel	0.036316	True	2016-11-01 00:00:38+00:00	This is Tucker. He's out here bustin h*ckin gh...	13	10	Tucker	NaN	6	3812	11780
1534	793500921481273345	https://pbs.twimg.com/media/CwMU34YWIAAz1nU.jpg	Golden_retriever	0.326122	True	Labrador_retriever	0.219904	True	Chesapeake_bay_retriever	0.163366	True	2016-11-01 17:12:16+00:00	This is Fiona. She's an extremely mediocre cop...	12	10	Fiona	NaN	5	2786	11953

	tweet_id	jpg_url	1st_prediction	1st_confidence	1st_dog	2nd_prediction	2nd_confidence	2nd_dog	3rd_prediction	3rd_confidence	3rd_dog	timestamp	tweet	rating_numerator	rating_denominator	name	dog_type	name_length	retweet_count	favorite_count
41	666701168228331520	https://pbs.twimg.com/media/CUCZLHlUAAAeAig.jpg	Labrador_retriever	0.887707	True	Chihuahua	0.029307	True	French_bulldog	0.020756	True	2015-11-17 19:35:19+00:00	This is a golden Buckminsterfullerene named Jo...	8	10	NaN	NaN	1	234	449
1358	761976711479193600	https://pbs.twimg.com/media/CpMVxoRXgAAh350.jpg	Labrador_retriever	0.475552	True	Chesapeake_bay_retriever	0.082898	True	Staffordshire_bullterrier	0.048464	True	2016-08-06 17:26:19+00:00	This is Shelby. She finds stuff to put on her ...	12	10	Shelby	NaN	6	2310	5992
1527	793195938047070209	https://pbs.twimg.com/media/CwH_foYWgAEvTyI.jpg	Labrador_retriever	0.654762	True	Golden_retriever	0.074100	True	Chihuahua	0.042339	True	2016-10-31 21:00:23+00:00	Say hello to Lily. She's pupset that her costu...	12	10	Lily	puppo	4	6547	17063
1522	793120401413079041	https://pbs.twimg.com/media/CwG6zDfWcAA8jBD.jpg	Labrador_retriever	0.724944	True	Golden_retriever	0.169744	True	Kuvasz	0.035502	True	2016-10-31 16:00:13+00:00	This is Robin. She's desperately trying to do ...	11	10	Robin	NaN	5	4551	14202
1508	790277117346975746	https://pbs.twimg.com/media/Cveg1-NXgAASaaT.jpg	Labrador_retriever	0.427742	True	Great_dane	0.190503	True	Curly-coated_retriever	0.146427	True	2016-10-23 19:42:02+00:00	This is Bruce. He never backs down from a chal...	11	10	Bruce	NaN	5	3732	14081

	tweet_id	favorite_count	jpg_url	dog_type	1st_prediction
1684	822872901745569793	132810	https://pbs.twimg.com/media/C2tugXLXgAArJO4.jpg	puppo	Lakeland_terrier
1218	744234799360020481	131075	https://pbs.twimg.com/ext_tw_video_thumb/74423...	doggo	Labrador_retriever

	tweet_id	jpg_url	1st_prediction	1st_confidence	1st_dog	2nd_prediction	2nd_confidence	2nd_dog	3rd_prediction	3rd_confidence	...	timestamp	tweet	rating_numerator	rating_denominator	name	dog_type	name_length	retweet_count	favorite_count	year
1543	795464331001561088	https://pbs.twimg.com/ext_tw_video_thumb/79546...	Golden_retriever	0.193082	True	Chesapeake_bay_retriever	0.157927	True	Soft-coated_wheaten_terrier	0.124684	...	2016-11-07 03:14:10+00:00	Elder doggo does a splash. Both 13/10 incredib...	13	10	NaN	doggo	4	27728	55683	2016
722	686034024800862208	https://pbs.twimg.com/media/CYVIToGWQAAEZ_y.jpg	Great_dane	0.236920	True	Irish_wolfhound	0.117608	True	Greater_swiss_mountain_dog	0.103900	...	2016-01-10 03:57:12+00:00	Your fav crew is back and this time they're em...	12	10	NaN	NaN	4	1324	3424	2016
1266	749774190421639168	https://pbs.twimg.com/media/Cme7pg2XEAATMnP.jpg	Pekinese	0.879012	True	Chihuahua	0.054855	True	Blenheim_spaniel	0.021041	...	2016-07-04 01:17:51+00:00	This is Lucy. She's a Benebop Cumberplop. 12/1...	12	10	Lucy	NaN	4	1493	5114	2016
1527	793195938047070209	https://pbs.twimg.com/media/CwH_foYWgAEvTyI.jpg	Labrador_retriever	0.654762	True	Golden_retriever	0.074100	True	Chihuahua	0.042339	...	2016-10-31 21:00:23+00:00	Say hello to Lily. She's pupset that her costu...	12	10	Lily	puppo	4	6547	17063	2016
1259	748977405889503236	https://pbs.twimg.com/media/CmTm-XQXEAAEyN6.jpg	German_short-haired_pointer	0.742216	True	Bluetick	0.152810	True	English_setter	0.051835	...	2016-07-01 20:31:43+00:00	What jokester sent in a pic without a dog in i...	10	10	Not	NaN	3	3759	11235	2016

TWITTER DATA ANALYSIS¶

INTRODUCTION¶

Data Wrangling¶

GATHERING THE DATA¶

1. Twitter Archive Enhanced¶

2. Image Predictions¶

3. Twitter¶

ASSESSING THE DATA¶

Tweeter Archive¶

Data Tidiness Issues¶

Quality Issues¶

Image Predictions¶

Data Tidiness Issues¶

Quality Issues¶

Tweet Data¶

Data Tidiness Issues¶

Quality Issues¶

DATA CLEANING¶

Twitter Archive¶

Step 1¶

Step 2¶

Step 3¶

Step 4¶

Step 5¶

Step 6¶

Step 7¶

Image Prediction¶

Step 1¶

Step 2¶

Step 3¶

Step 4¶

Tweet Data¶

Step 1¶

Cojoin Data Sources¶

Step 1¶

Step 2¶

Step 3¶

Storing Final Data File As A CSV File¶

DATA VISUALIZATION¶

Show The Dynamics of Dog Breeds Against Dog Predictions¶

Show The Top Five Images Of The Two Most Popular Dog Breeds¶

1. The Top 5 Golden Retrievers¶

No.1 Golden Retriever¶

No.2 Golden Retriever¶

No.3 Golden Retriever¶

No.4 Golden Retriever¶

No.5 Golden Retriever¶

2. The Top 5 Labradors¶

No.1 Labrador Retriever¶

No.2 Labrador Retriever¶

No.3 Labrador Retriever¶

No.4 Labrador Retriever¶

No.5 Labrador Retriever¶

Show Popularity of Dog Types Against Dog Predictions¶

Show The Top 2 Images Of The Most Liked Dog Type¶

No.1 Most Liked Dog Type¶

No.2 Most Liked Dog Type¶

Show Voter Distribution In The Time Period Which The Data Was Gathered¶

Show The Virality (Spreading) Distribution Of Dog Favorites In The Form Of Retweets¶

Conclusions¶